The present exemplary embodiments relate to control systems and diagnosis systems thereof for fault diagnosis in production plants that include multiple resources for achieving production goals. Automated diagnosis of system performance and component status can advantageously aid in improving productivity, identifying faulty or underperforming resources, scheduling repair or maintenance, etc. Accurate diagnostics requires information about the true condition of components in the production system, which can be obtained directly from sensors associated with individual components and/or may be inferred from a limited number of sensor readings within the production plant using a model or other knowledge of the system structure and dynamics. Complete sensor coverage for all possible system faults is generally cost prohibitive and/or impractical in harsh production environments, and thus it is generally preferable to instead employ diagnostic procedures to infer the source of faults detected or suspected from limited sensors. Conventional automated diagnosis systems focus on a single set of assumptions regarding fault possibilities, for example, where only single persistent faults are assumed. Complex diagnostic assumptions, while generally able to correctly identify a wider range of fault conditions, are computation intensive and thus expensive to implement. Over simplified assumptions, however, may not be able to accurately assess the condition of the production system and its components. Accordingly, a need remains for improved control and diagnostic systems and techniques by which automated diagnosis can be performed in an accurate and efficient manner to determine a current plant condition for a production system having only limited sensor coverage.